Data Syntesis - Cohort analysis

Introduction

This R notebook is a rapid analysi of the cohort data produced as part of the data synthesis of citizen science projects

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Data

Lets load in the data, it is currently seperate sheets in an excel file. Lets pull out the seperate sheets and assign to data.frames

summary <- read_excel(path = '../data/BES citsci data summary v3.xlsx', sheet = 1)
metadata <- read_excel(path = '../data/BES citsci data summary v3.xlsx', sheet = 2)
retention_tasks <- read_excel(path = '../data/BES citsci data summary v3.xlsx', sheet = 3)
retention_time <- read_excel(path = '../data/BES citsci data summary v3.xlsx', sheet = 4)
ppt_inequality <- read_excel(path = '../data/BES citsci data summary v3.xlsx', sheet = 5)

I think I need to spread the cohort data so that time is represented in columns

ret <- pivot_wider(data = retention_time, names_from = 'Session', values_from = 'NumberOfPeople')
head(ret)

# easy to view but not he format for ggplot

retention_time$CohortSession <- (retention_time$Session + 1) - retention_time$Cohort 
retention_time$ProjectCohort <- paste(retention_time$ProjectCode, 
                                      retention_time$Cohort,
                                      sep = '_')

That works. So now can we do an initial visualisation of the data

p <- ggplot(retention_time, aes(x = CohortSession, y = NumberOfPeople, group = ProjectCohort)) +
  geom_line(aes(colour = ProjectCode))
ggplotly(p)

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Okay now we need to average across cohorts in the same project and rescale so they all start with the same value

# Rescale
rescale <- function(x){
  
  (x/max(x)) * 100
  
}

retention_time$NumberOfPeopleRescaled <- NA

for(i in unique(retention_time$ProjectCohort)){
  
  retention_time$NumberOfPeopleRescaled[retention_time$ProjectCohort == i] <-
    rescale(retention_time$NumberOfPeople[retention_time$ProjectCohort == i])
  
}


p <- ggplot(retention_time, aes(x = CohortSession, y = NumberOfPeopleRescaled, group = ProjectCohort)) +
  geom_line(aes(colour = ProjectCode))
ggplotly(p)

And now average across projects

# group and average
av_ret <- tapply(retention_time$NumberOfPeopleRescaled,
                 INDEX = list(retention_time$ProjectCode,
                              retention_time$CohortSession),
                 FUN = mean)

# Put into long format
av_ret <- melt(av_ret, varnames = c('ProjectCode', 'CohortSession'), value.name = 'NumberOfPeopleRescaled')
av_ret

# plot
p <- ggplot(av_ret, aes(x = CohortSession, y = NumberOfPeopleRescaled,
                        group = ProjectCode)) +
  geom_line(aes(colour = ProjectCode))
ggplotly(p)

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library(plotly)
p <- plot_ly(economics, x = ~date, y = ~unemploy / pop)
p

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